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scientists are human beings. And so all the good and the bad about humans,
and how they make their choices, and what they value, carries over to the
scientific and academic enterprise. It's not different. It's a lovely guild and it's
functioned fantastically for centuries, and I hope it continues to function for
a long time because I think it has been very good for the species, but we
shouldn't believe that scientists are somehow not subject to the same joys
and distractions as every other human being.
So that's part of what I learned—that science is somehow not a qualitatively
different enterprise than, let's say, technology or any other difficult human
endeavor. These are very difficult human endeavors, and they take planning,
and attention, and care, and execution, and they take a community of people
to support it. Everything I just said is completely true of academic science,
writing papers, winning grants, training students, teaching students, as well
as forming a new company, doing research, or using technology in a big
corporation that's already been established. All of those things are difficult
and require a community of people to make it happen. As they say: “people,
ideas, and then things, in that order” That's true in any science and that's also
true in the real world.
Gutierrez:
What does the future of data science look like?
Wiggins:
I don't see any reason for data science not to follow the same
course as many other fields, which is that it finds a home in academia, which
means that there becomes a credentialing function, particularly around pro-
fessional subjects. You'll get master's degrees and you'll get PhDs. The field
will take on meaning, but it will also take on specialization. You see this already
with people using the phrases “data engineering” and “data science” as sepa-
rate things. My group here at The New York Times is the Data Science group,
which is part of the Data Science and Engineering larger group. People are
starting to appreciate how a data science team involves data science, data
engineering, data visualization, and data architecture.
Data Product is not sort of a thing yet, but certainly, if you look at how, say,
data science happened at LinkedIn—data science reported up through the
product hierarchy. At other companies, data science reports through business;
or it reports through engineering. Right now I'm located within in the engi-
neering function of The New York Times, separate from the product, separate
from marketing, and separate from advertising. Different companies are locat-
ing data science in different arms.
So I think there'll be credentialing. I think there will be specialization. New
fields are born—I wouldn't say all the time, because by real-world standards,
nothing ever happens in academia—but there are new departments born at
universities every few years. It happens, and the way that it happens is part of
the creation of new fields. I'm old enough that I had the benefit of watching,
say, systems biology be born as a field, synthetic biology be born as a field,
